A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems

Kawahara, Yoshinobu, Yairi, Takehisa, Machida, Kazuo

Dec-31-2007–Neural Information Processing Systems

We construct the theoretical underpinning and derive a concrete algorithm for nonlinear identification.

artificial intelligence, machine learning, matrix, (14 more...)

Neural Information Processing Systems

Dec-31-2007

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Country:
- Asia
  - Japan > Honshū
    - Kantō > Tokyo Metropolis Prefecture > Tokyo (0.05)
  - Middle East > Jordan (0.04)
- Europe
  - Germany > Baden-Württemberg
    - Tübingen Region > Tübingen (0.04)
  - Netherlands > South Holland
    - Dordrecht (0.04)
- North America > United States
  - California > San Francisco County > San Francisco (0.14)

Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)

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A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems

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